Security of Deep Learning based Lane Keeping System under Physical-World Adversarial Attack
Year: 2020
Type: preprint
Abstract: Lane-Keeping Assistance System (LKAS) is convenient and widely available today, but also extremely security and safety critical. In this work, we design and implement the first systematic approach to attack real-world DNN-based LKASes. We identify dirty road patches as a novel and domain-specific threat model for practicality and stealthiness. We formulate the attack as an optimization problem, and address the challenge from the inter-dependencies among attacks on consecutive camera frames. We evaluate our approach on a state-of-the-art LKAS and our preliminary results show that our attack can successfully cause it to drive off lane boundaries within as short as 1.3 seconds.
Source: arXiv (Cornell University)
Authors: Sato, Takami, Shen, Junjie, Wang, Ningfei, Jia, Yunhan Jack, Lin, Xue, Chen, Qi Alfred
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3010105477